强化学习用于闭环调节心血管系统与迷走神经刺激:一个计算研究
Parisa Sarikhani1, Hao-Lun Hsu2, Mahmoud Zeydabadinezhad1
1Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America.
Journal of neural engineering
|May 8, 2024
概括
人工智能 (AI) 和强化学习 (RL) 创建了自适应的闭环迷走神经刺激 (VNS) 系统. 这些人工智能驱动的VNS方法系统地优化了对潜在疾病治疗的心血管控制.
科学领域:
- 计算心血管科学是一种心血管科学.
- 生物医学工程 生物医学工程
- 医学中的人工智能.
背景情况:
- 神经刺激 (VNS) 显示出治疗心血管疾病的前景,如心力衰竭,心律失常和高血压.
- 目前的VNS参数控制缺乏系统的优化,阻碍了治疗疗效.
- 人工智能 (AI) 和闭环系统为适应性,数据驱动的VNS控制提供了一条道路.
研究的目的:
- 开发和评估一个使用强化学习 (RL) 的交互式AI框架,用于闭环VNS控制系统的自动化,数据驱动的设计.
- 调查AI在系统学习和适应心血管调节最佳VNS参数方面的能力.
- 评估基于RL的控制在模拟各种生理状态下的健康和高血压心血管系统的性能.
主要方法:
- 开发了多个模拟环境和一个标准的API来评估闭环VNS控制系统.
- 在休息和运动状态下利用基于生物物理学的老鼠心血管系统 (健康和高血压) 的计算模型.
- 实施了基于RL的控制框架,用于心率和平均动脉压设定点跟踪,比较深度RL和自适应概率推理策略.
主要成果:
- 闭环RL方法成功地学习了最佳VNS控制策略,并适应了不断变化的设定点和系统动态.
- 证明了样本效率和通用性之间的权衡,为VNS控制提供了算法选择的信息.
- 转移学习提高了深度RL算法的样本效率,为更个性化的VNS系统铺平了道路.
结论:
- 基于RL的闭环VNS系统能够有效控制心血管.
- 本文介绍的AI框架提供了一种系统的,可适应的方法,可以在没有先前动态知识的情况下学习VNS控制策略.
- 这种方法促进了针对心血管疾病的高效和个性化的VNS疗法的开发.
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